ORIGINAL ARTICLE Open Access

Intestinal microbiota and functionalcharacteristics of black soldier fly larvae(Hermetia illucens)Yuan Zhineng1,2, Ma Ying1,3, Tang Bingjie1, Zeng Rouxian1 and Zhou Qiang1*

Abstract

Purpose: Black soldier fly transforms organic waste into insect protein and fat, which makes it valuable forecological utilization. This process is associated with the intestinal microbiota. This research was developed todetermine the type and functional characteristics of intestinal microbiota present in black soldier fly larvae.

Methods: In this research, metagenomics has been used to study black soldier fly larvae gut bacteria, whichinvolves the high abundance of the gut microbe advantage bacterium group, the impact, and the physiologicalfunctions of the microbiota. Furthermore, intestinal bacteria and their related functions were investigated bybioinformatics analysis to evaluate potential microbial strains that may be used to improve feed utilization efficiencyin factory farming.

Result: The results showed that black soldier fly larvae’s intestine contains more than 11,000 bacteria. The highrelative abundance of group W (larvae fed with 75% wheat bran and 25% soybean powder) may promote feedutilization efficiency, whereas high relative abundance of group T microbiota (larvae fed with 75% wheat bran and25% soybean powder supplemented with 1% tetracycline) may play an important role in black soldier fly larvaesurvival.

Conclusion: The gut bacteria in black soldier fly larvae were involved in polysaccharide biosynthesis andmetabolism, translation, membrane transport, energy metabolism, cytoskeleton, extracellular structures, inorganicion transport and metabolism, nucleotide metabolism, and coenzyme transport physiological processes. The 35significant differential microbes in group W may have a positive impact on feed utilization and physiologicalprocess.

Keywords: Hermetia illucens, Intestinal bacteria, Utilization efficiency, Metagenomics

BackgroundBlack soldier fly Hermetia illucens (Diptera: Stratiomyidae)larvae are commonly used to recycle organic waste (vanHuis 2013). They feed on organic waste including live-stock manure (Rehman et al. 2017; Beskin et al. 2018),food waste (Nguyen et al. 2015), and organic waste (Liet al. 2011). The black soldier fly is currently used as a toolfor waste transformation and utilization, but few

researches consider diverse protocols to improve its wasteutilization efficiency. Intestinal microorganisms are in-volved in several insect functions, including nutritionalcoordination (Douglas 1998), defense against plant toxins(Hammer and Bowers 2015), physiological response(Basset et al. 2000; Li et al. 2016), life span increase(Hoyt et al. 1971), influence on the development andreproductive potential (Gavriel et al. 2011; Prado andAlmeida 2009), and detoxifying of specific foods(Hehemann et al. 2010), among others. Collectively,insect intestinal microorganisms play a crucial role inthe growth and development of insects. Other studies

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* Correspondence: lsszhou@mail.sysu.edu.cn1The School of Life Sciences, Sun Yat-sen University, Guangzhou 510275,ChinaFull list of author information is available at the end of the article

Annals of MicrobiologyZhineng et al. Annals of Microbiology (2021) 71:13 https://doi.org/10.1186/s13213-021-01626-8

have shown that bacteria in insects’ gut play an importantrole in promoting food utilization. In this regard, gut bac-teria of wood-feeding higher termite promote celluloseand xylan hydrolysis (Warnecke et al. 2007), whereasOdontotaenius disjunctus intestinal microbiota contributesto lignocellulose decomposition (Ceja-Navarro et al.2019). Studies of black soldier fly larvae gut microbes in-clude antibacterial peptide active substances extraction(Park et al. 2015), intestinal specific microbiota (Xie 2010),conserved microbiota analysis (Shelomi et al. 2020), andactive enzymes analysis and identification (Kim et al.2011; Lee et al. 2014, 2016). A recent review evaluatedblack soldier fly larva microbial community and pros-pected its feasibility to utilization improvement (De Smetet al. 2018). Furthermore, a relevant research finished anin-depth study to classify the positive influence of the gutmicrobiota of black soldier fly larvae (Bruno et al. 2019).However, there is scarce research focused on biologicalfunction analysis of black soldier fly larvae’s gut micro-biota. In addition, no research has been performed tosearch for microbiota that enhance feed utilization effi-ciency. It then becomes necessary to develop research tobetter understand the black soldier fly larvae’s microbiotacharacteristics and functions.Metagenomics research is a useful tool in gut microbe

research, whose information is based on sequencingdata (Furrie 2006). Based on the development of thistechnology, many unculturable microbes have beenstudied and analyzed (Handelsman 2004). In this con-cern, metagenomics research has expanded the min-ing of microbial community structure and function inthe environment. To evaluate the black soldier fly lar-vae’s intestinal bacteria biological function and screenthe potential microbial strain that may be exploitedto improve feed utilization efficiency in factory farm-ing, such bacteria were systematically analyzedthrough metagenomics.

ResultsData quality assessmentRaw reads obtained from contig sequencing were assem-bled with the software MEGAHIT (Gurevich et al. 2013)in default parameter. Contig sequences shorter than 300bp were discarded, and the assembly results were evalu-ated by QUAST (Zhu et al. 2010) with the default par-ameter. The results demonstrated that the number ofcontig in different experimental groups was about 600,000 and greatly varied in length. The N50 length wasabout 1000–1200 bp with the alignment rate exceeding95% (Table 1).MetaGeneMark (Fu et al. 2012) was used to identify

coding regions in the genome with default parameters.Gene prediction was conducted according to the assem-bled contigs. The number of genes in different sampleswas about 500,000. The average gene length measuredin each sample was in the range of 260 to 340 bp. TheCd-hit software (version 4.6.6) was used to remove theredundancy with default parameters; the similaritythreshold was set at 95% and the coverage threshold at90% (Fu et al. 2012). It was concluded that there were 2,168,041 non-redundant genes obtained in this sequenceprocess, with an average length of 280 bp (Table 2).

Species information statisticsThe species composition and relative abundance of thesamples were obtained by comparing the above non-redundant genes with the species information of the se-quence in the Nr database (Ashburner et al. 2000). Theintestinal microbial species of the black soldier fly larvaein different groups were counted in taxa of the kingdom,phylum, class, order, family, genus, and species. The gutlarvae microbiota was abundant, reaching more than2300 genera and 11,000 species. The number of micro-bial species on different taxa in different groups wasrelatively similar, but a significant difference was

Table 1 Assembled data assessment

Sample Total length (bp) Contig number Largest length (bp) GC (%) N50 (bp) Mapped (%)

F1 713,374,913 679,554 332,933 41.78 1115 97.07

F2 601,410,151 594,324 264,723 41.58 1061 95.68

F3 621,800,601 613,040 315,309 41.63 1065 95.84

T1 573,217,820 519,415 184,590 41.85 1194 96.05

T2 716,521,041 613,618 721,453 41.86 1290 97.17

T3 649,395,570 557,814 186,681 42.01 1287 96.87

W1 604,749,023 581,793 363,719 42.28 1072 95.93

W2 712,670,354 668,870 399,518 42.11 1116 96.38

W3 680,722,887 639,170 744,843 42.37 1108 96.35

Sample is the sample number; total length is the sum of the base numbers of all contigs; contig numbers are the numbers of contigs after assembly; largestlength is the number of bases in the longest contig; N50 are contigs that were sorted from long to short, and the cumulative length was counted. When a contigwas added and the cumulative length was equal to half of the sum of the lengths of all contigs, the length of the contig was N50; Mapped is the alignment ratebetween sequencing reads and assembled contigs

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observed in genus between groups W and T. In general,the intestinal microbe species of group W were higher,but not in a significant level than those of groups F andT (Table 3).

Analysis of high-abundant bacteria in the intestinal tractof black soldier fly larvaeThe resulting top 15 high abundance bacteria were se-lected for comparison. The results showed that the rela-tively high abundance of bacteria at the level of phylumand genus was highly comparable (Fig. 1). The highestrelative abundance of bacteria belonged to Proteobac-teria, Firmicutes, Bacteroidetes, and Actinobacteriaphyla. The highest abundance of bacteria at the specieslevel were Enterococcus, Acinetobacter, Providencia, En-terobacter, and Myroides.

Similarity analysis of microbial and functional genesbetween groupsThe microbiota and functional gene composition of thesamples were hierarchically clustered through R and theunweighted paired average method (UPGMA). Based onthis, the similarity of species composition and functionalgene composition of each sample was determined. Thesample distance in the sample hierarchy clustering indi-cates the similarity of the species composition of the twosamples. The results showed that samples in the samegroups are closer and the branches are shorter; there-fore, the microbial species structure and functional gene

composition of the samples between the same treatmentgroups were comparable (Fig. 2a, c).Principal component analysis (PCA) decomposes the

differences of multiple sets of data on the two-dimensional coordinate chart through processing complexdata into two eigenvalues. Composition analysis of differ-ent samples (97% similarity) reflects differences and dis-tances between samples. The composition of the speciesin the two samples is similar, when they come closer on aPCA diagram. The results showed that the same samplein the same group was closer to each other, which demon-strated that the microbial composition and functionalgene composition were comparable. The sum of the firstdimension (98.4%) and the second dimension (1.13%)reached 99.53%, which explains the difference betweenthe different groups to a great extent (Fig. 2b, d).

Analysis of differential microbesAbout 100 species were selected (p < 0.05) to draw differen-tial heatmaps. According to the heatmap, there exists 35high relative abundance species in group W (Faecalicatenacontorta, Stenotrophomonas acidaminiphila, Sphingobacter-ium cellulitidis, Salana multivorans, Enterococcus sp.Gos25−1, Lachnospiraceae bacterium OF09-33XD, Pusilli-monas caeni, Hungatella hathewayi, Sphingobacterium sp.30C10-4-7, Stenotrophomonas sp. Leaf70, Pusillimonas sp.T7-7, uncultured Stenotrophomonas sp., Frischella perrara,Myroides sp. N17-2, Microbacterium sp. CH12i, Ochrobac-trum sp. A44, Microbacterium ginsengiterrae, Kluyvera

Table 2 Predicted genes overview

Sample ID Gene number Total length (bp) Average (bp) Max length (bp) Min length (bp)

F1 550,158 152,574,384 277 7536 102

F2 464,889 125,423,319 269 9972 102

F3 485,384 132,226,104 272 7536 102

T1 426,498 117,609,837 275 8286 102

T2 531,807 152,545,194 286 8175 102

T3 472,169 132,783,018 281 8904 102

W1 506,309 164,800,824 325 12,033 102

W2 579,598 184,957,656 319 10,866 102

W3 576,423 195,543,927 339 11,607 102

Gene set 2,168,041 608,038,053 280 12,033 102

Samples is the sample number; gene numbers is the number of predicted genes; total length is the base sum of predicted genes; average length is the averagebases of predicted genes

Table 3 Microbial species statistics

Group Kingdom Phylum Class Order Family Genus Species

F 6 115.33 ± 1.15 124.67 ± 1.15 269.67 ± 1.53 623.33 ± 0.58 2309.67 ± 9.61 ab 11,510.33 ± 25.50

T 6 115.33 ± 1.53 125.33 ± 0.58 269.33 ± 2.08 619 ± 5.20 2283 ± 7.81 a 11,432.33 ± 68.72

W 6 111.67 ± 1.15 125.33 ± 1.15 269.67 ± 0.58 622 ± 3.46 2321.67 ± 10.11 b 11,880 ± 17.09

Data are indicated ad means ± standard error. Non-parametric ANOVA was used to analyze the data in the same column. Different letters in the same columnindicate a significant difference(p < 0.05) between the groups

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georgiana, Sphingobacterium gobiense, Myroides odoratus,Klebsiella pneumoniae, Enterococcus pallens, Sphingobac-terium lactis, Candidatus Schmidhempelia bombi, Aequori-vita soesokkakensis, Vitellibacter aquimaris, Enterococcussp. 9D6 DIV0238, Providencia stuartii, Miniimonas sp.PCH200, Gilliamella apicola, Orbus hercynius, Sphingobac-terium mizutaii, Sphingobacterium sp. 1.A.4, Microbacter-ium profundi, Thermus filiformis), 34 in group T(Dysgonomonas capnocytophagoides, Allomyces macrogy-nus, Enterococcus sp. 9E7 DIV0242, Candidatus Erwiniadacicola, Enterococcus sp. 4G2 DIV0659, Propionibacteria-ceae bacterium 16Sb5-5, Desulfovibrio sp. DS-1, Marinifi-lum breve, Tatumella sp. UCD-D suzukii, Bacillusvelezensis, Staphylococcus gallinarum, Staphylococcus sciuri,Dysgonomonas sp. Marseille-P4361, Klebsiella aerogenes,Providencia rettgeri, Mycolicibacterium mucogenicum,Corynebacterium nuruki, Ruaniaceae bacterium KH17,Corynebacterium stationis, Enterococcus sp. 6C8DIV0013, Enterococcus faecium, Providencia rustigia-nii, Wohlfahrtiimonas populi, Weissella jogaejeotgali,Bacillus amyloliquefaciens, Enterococcus casseliflavus,Bacteroides thetaiotaomicron CAG:40, Weissella thai-landensis, Enterobacter hormaechei, Staphylococcusxylosus, Enterococcus saccharolyticus, Carnobacteriummaltaromaticum, Spizellomyces punctatus), and 22 ingroup F (Citrobacter sp. MH181794, Schaalia canis,Metarhizium majus, Candida maltose, Nosocomiicoccusmassiliensis, Lactobacillus sp. 54-5, Dysgonomonas gadei,Dorea longicatena, Prevotella sp. 10(H), Leminorella

grimontii, Bacteroides caccae, Variovorax sp. EL159, Soli-bacillus isronensis, Proteus mirabilis, Actinomyces sp.,Staphylococcus lentus, Rozella allomycis, Ignatzschineriasp. F8392, Leucobacter triazinivorans, Campylobacter con-cisus, Bacteroides sp. 1 1 14, Azospira oryzae). The rela-tively high abundance species of the different groupsconcentrate on different species without obvious overlapin the heatmap. Although the relatively high abundancespecies of the differential species in group F was the low-est, the relatively low abundance species were also lessthan the other two groups (Fig. 3).

Analysis of differential functional geneThe heatmap of different functional genes was obtainedthrough a parametric test. The heatmap of the Kegg(Kanehisa et al. 2004) metabolic pathways in differentialabundance gene shows that groups possess differencesin polysaccharide biosynthesis and metabolism, transla-tion, membrane transport, and energy metabolism.Group W results showed the highest relative abundancein four biological processes, group F maintained middle-level abundance in those four-biological process, andgroup T showed low abundance in those four biologicalprocesses (Fig. 4a). The EggNOG (Powell et al. 2014)heatmap evidences the differences of the cytoskeleton,extracellular structures, inorganic ion transport and me-tabolism, nucleotide transport and metabolism, and co-enzyme transport and metabolism in different groups.Group W showed a high abundance in extracellular

Fig. 1 Relative abundance microbiota analysis in the phylum and genus. a Relative abundance microbiota analysis in the phylum. b Relativeabundance microbiota analysis in the genus. F1, F2, and F3 are replicates of group F; T1, T2, and T3 are replicates of group T; W1, W2, and W3 arereplicates of group W

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structure, inorganic ion transport and metabolism, nu-cleotide metabolism and transport, and coenzyme trans-port and metabolism, whereas the related functional genesof the cytoskeleton in group T showed relatively highabundance. The extracellular structures of group F werecomparable to those of group W, whereas the other cat-egories were less than those of group W (Fig. 4b).

DiscussionThe N50 length of assembly sequence was about 1100bp, with an alignment rate exceeding 95%, whereas thecontig number of a single sample reached 600,000,which means that this data assembly quality meets therequirement of research and analysis. Gene number of asingle sample up to 550,000 suggests that the sequencingdepth is enough for subsequent microbial biological ana-lysis. Moreover, the results of intestinal microbial statis-tics showed that there were over 11,000 intestinal

bacteria in the black soldier fly larvae gut, indicating thepresence of highly abundant microbes in the gut. It isrelatively similar among different groups in each taxo-nomic order, indicating that different feeds had a limitedinfluence on the overall microbial community. The top15 high abundance microbes in the class and genus ana-lysis results demonstrated that there was no apparentdifference between the groups. The results indicate thatfeed played a slight role in the core gut microbiota ofblack soldier fly larvae, which was consistent with pub-lished research (Klammsteiner et al. 2020). The highestrelative abundance bacteria belonged to the Proteobac-teria, Firmicutes, Bacteroidetes, and Actinobacteriaphyla. It showed some differences with Jeon et al.’s(2011) results, which included Bacteroidetes, Proteobac-teria, Firmicutes, Fusobacteria, and Actinobacteria.Among those bacteria, Proteobacteria was observed as apotential microbial signature of disease, Firmicutes was

Fig. 2 Analysis of the similarity of microbial and functional gene composition. a UPGMA (unweighted pin-group method with arithmetic mean)cluster analysis of microorganisms among samples. b Principal component analysis of microbial composition between samples. c UPGMA analysisof functional gene composition among samples. d Principal component analysis of microbe composition between samples

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considered to play an important role in the digestion ofanimal manure, and Bacteroidetes were shown to de-grade high-molecular weight organic matter (Zhan et al.2020). Taken together, the larvae gut core microbiota fa-cilitated degradation of animal manure and organic mat-ter, which may provide microbial evidence for Kimet al.’s (2011) research. However, we should be morecautious to adopt black soldier larvae protein as ediblefor Proteobacteria in its gut (Wang and Shelomi 2017).

The intergroup similarity analysis of microbial speciesand functional genes demonstrates that both feeds andtetracycline had an impact on the intestinal microbialcommunity structure of the black soldier fly larvae. Theintestinal microbial community structure of larvae fedunder a similar environment was comparable, whichagrees with other studies (Jiang 2018; Tanaka et al. 2006;Liang et al. 2014; Bruno et al. 2019). Furthermore, ourresults agreed with (Engel et al. 2013) in that the diet

Fig. 3 The heatmap of differential microbe in species. The x-axis represents the different treatment replicates; the y-axis represents the differentialmicrobe in different replicates

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shapes the composition and activity of the gut micro-biota. Heatmap analysis results demonstrated that differ-ent treatments influence some microbes in the blacksoldier fly larvae. A similar trend shown in functiongenes indicates a relation between differential microbesand function genes. We can then infer that those mi-crobes play a crucial role in black soldier fly larvaegrowth and development, which agrees with De Smetet al.’s (2018) conclusion. From these results, we mayconclude the relatively high abundance microbiota ingroup W (35) and group F (22) play an important role ingrowth and development, whereas the relatively highabundance microbiota in group T (34) are relevant forblack soldier fly larvae to survive in harsh conditions.However, further research is required to address suchassumptions. Combining heatmap of Kegg and eggNOGanalysis in differential function genes showed that rela-tive function gene abundance of groups W, F, and Tgradually reduces polysaccharide biosynthesis and me-tabolism, translation, and membrane transport and en-ergy metabolism which associate with feeding. Hereby,we inferred that the 35 differential microbiota in groupW were closely related to feeding. Furthermore, we be-lieve that the differential microbiota in different groupsplay an important role in such biological process. GroupT has a relatively high abundance of functional genes inthe cytoskeleton indicating that the relatively high abun-dance of differential microbiota in group T was relatedto the cytoskeleton, which may be due to the inhibitoryeffect of tetracycline on some essential microbes

involved in the development and feeding (Cai et al.2018; Cifuentes et al. 2020), showing a great differencewith mycotoxins and pesticides (Purschke et al. 2017).However, naval approaches are necessary to investigatethis issue. The functional gene in group W displayed ahigh level in cell structure, inorganic ion transport andmetabolism, nucleotide metabolic process, and coen-zyme transport and metabolism, which indicate that therelatively high abundance of differential microbiota ingroup W is essential in these biological processes. Basedon the aforementioned result, we conclude that the 35differential microbes in group W were mainly associatedwith energy metabolism which influences growth a lot,whereas the 34 differential microbes in group T were re-lated to survival in harsh environments. The presentstudy provided a better understanding of the intestinalmicrobiota of black soldier fly larvae; however, it did notfully address the relation between microbes and func-tion. We have research underway to elucidate such arelationship.

ConclusionThe present study demonstrated that gut bacteria in blacksoldier fly larvae were involved in polysaccharide biosyn-thesis and metabolism, translation, membrane transport,energy metabolism, cytoskeleton, extracellular structures,inorganic ion transport and metabolism, nucleotide me-tabolism, and coenzyme transport physiological processes.The 35 significant differential microbes in group W have apositive impact on energy metabolism and physiological

Fig. 4 Analysis of difference groups between functional genes. a Heatmap of differentially functional gene abundance in the Kegg metabolicpathway. b Heatmap of differentially functional genes in the eggNOG metabolic pathway. Genes shown have p values < 0.05, after metagenomesequencing analysis

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process which could be exploited to improve transform ef-ficiency. Subsequent studies will explore the function ofspecific differential high relative abundance microbes ingroup W and evaluate the possibility of improving farmingefficiency with those microbiotas.

MethodsThe research aims at exploring the relation betweenblack soldier fly larvae and its intestinal microbiota.Fourth to 5th stage black soldier fly larvae fed with var-ied feeds were selected to study the intestinal micro-biota. In order to avoid accidental outcomes, we set 3repeats (10 individuals for 1 repeat) for every treatment.The metagenomic method which is a practical tool wasutilized to achieve our goal.

Insect sources and breedingBlack soldier fly eggs and larvae were partly acquiredfrom Guangzhou Anruijie Environmental ProtectionTechnology Limited company, all of them were trappedin a field and reared in 25 °C, 16 h photoperiod, 60–70%relative humidity feed for 30 generations.After hatching from eggs, black soldier fly larvae were

incubating with 75% wheat bran, 25% soybean powder(group W), food waste (group F), or 75% wheat bran and25% soybean powder supplemented with 1% tetracycline(group T) for 10 days. Then, 10 4–5th stage larvae wereselected in each group for intestinal anatomy, perform-ing triplicate determinations.

Intestinal dissection of black soldier fly larvaeBlack soldier fly larvae were starved for 24 h, after whichthey were washed with sterile water and inactivated for10 min at − 20 °C. Next, they were surface sterilized with75% alcohol and washed with sterile water. Larva intes-tines were dissected out with sterile scissors and twee-zers on a sterile operating table, eliminating intestinaladhesions. The obtained larvae intestines were preservedat − 80 °C for subsequent sequencing at Beijing Bio-marker Technologies.

Metagenomic sequencing and bioinformatics analysisDNA isolation, sequencing, and bioinformatics analysiswere performed by Beijing Biomarker Technologies, ac-quiring 10 G of sequencing data for every replicate(three replicate determinations were achieved). Filteringand quality controlling were processed to get originalclean reads for subsequent analysis. The Trimmomaticsoftware was used for original splicing sequences (rawtags), whereas Bowtie2 was used to sequence alignmentwith the host genome to remove host contamination.MEGAHIT (Gurevich et al. 2013) was utilized for meta-genome assembly, and contig sequences shorter than300 bp were not considered. QUAST (Zhu et al. 2010)

was used to evaluate assembly results, whereas Meta-GeneMark (Fu et al. 2012) was used to predict the en-coding genes and perform functional annotation on theencoding genes in general database and special database.Those bioinformatics analysis tools were kept in defaultparameter while analysis performing. Taxonomic ana-lysis was performed based on clean reads data. Further-more, species composition, abundance information, andfunction genes of the samples were statistically analyzed.

Statistical analysesThe statistics in this research were analyzed in PrismGraphPad 8; the figures were drawn through R.

AcknowledgementsWe thank Beijing Biomarker Technologies for providing the data analysisservices. The authors would like to express their gratitude to EditSprings(https://www.editsprings.com/) for the expert linguistic services provided.

Authors’ contributionsAll authors contributed to the study conception and design. The first draft ofthe manuscript was written by Yuan Zhineng, and all authors commentedon previous versions of the manuscript. All authors read and approved thefinal manuscript.

FundingThe National Key R&D Program of China (grant number 2017YFF0210204)supported this research but was not involve in the design of the study;collection, analysis, and interpretation of the data; and writing of themanuscript.

Declarations

Ethics approval and consent to participateNo approval of research ethics committees was required to accomplish thegoals of this study because experimental work was conducted with anunregulated invertebrate species. The larvae were utilized to experimentwithout abuse or maltreatment. All authors understand that my participationis voluntary and agree to participate in the above research.

Consent for publicationWritten informed consent for publication was obtained from all participants.

Competing interestsThe authors declare that they have no competing interests.

Author details1The School of Life Sciences, Sun Yat-sen University, Guangzhou 510275,China. 2Guangdong Agribusiness Tropical Agriculture Institute, Guangzhou510000, China. 3The School of Agriculture, Sun Yat-sen University,Guangzhou 510275, China.

Received: 27 November 2020 Accepted: 22 February 2021

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